
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DonutImageProcessor




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def __init__(   self						:List[str]	,  a						:Optional[Any]	,  a						:Optional[Any]=7	,  a						:List[Any]=3	,  a						:Union[str, Any]=1_8	,  a						:Any=3_0	,  a						:Optional[int]=4_0_0	,  a						:Union[str, Any]=True	,  a						:List[Any]=None	,  a						:Union[str, Any]=True	,  a						:List[str]=False	,  a						:int=True	,  a						:str=True	,  a						:Optional[Any]=[0.5, 0.5, 0.5]	,  a						:Tuple=[0.5, 0.5, 0.5]	,  )						->					Dict:
								__UpperCamelCase      :  Union[str, Any]       =   parent
								__UpperCamelCase      :  str       =   batch_size
								__UpperCamelCase      :  Union[str, Any]       =   num_channels
								__UpperCamelCase      :  List[Any]       =   image_size
								__UpperCamelCase      :  Any       =   min_resolution
								__UpperCamelCase      :  Union[str, Any]       =   max_resolution
								__UpperCamelCase      :  List[str]       =   do_resize
								__UpperCamelCase      :  Dict       =   size if size is not None else {"height": 1_8, "width": 2_0}
								__UpperCamelCase      :  str       =   do_thumbnail
								__UpperCamelCase      :  Any       =   do_align_axis
								__UpperCamelCase      :  Any       =   do_pad
								__UpperCamelCase      :  Dict       =   do_normalize
								__UpperCamelCase      :  Tuple       =   image_mean
								__UpperCamelCase      :  Dict       =   image_std







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					int:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	DonutImageProcessor if is_vision_available() else None







	def   _lowerCamelCase							(   self						:int		)						->					Dict:
								__UpperCamelCase      :  List[str]       =   DonutImageProcessingTester(self		)







	@property
	def   _lowerCamelCase							(   self						:List[str]		)						->					Optional[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()







	def   _lowerCamelCase							(   self						:List[str]		)						->					Optional[int]:
								__UpperCamelCase      :  Union[str, Any]       =   self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(a	,  "do_resize"		)		)
								self.assertTrue(hasattr(a	,  "size"		)		)
								self.assertTrue(hasattr(a	,  "do_thumbnail"		)		)
								self.assertTrue(hasattr(a	,  "do_align_long_axis"		)		)
								self.assertTrue(hasattr(a	,  "do_pad"		)		)
								self.assertTrue(hasattr(a	,  "do_normalize"		)		)
								self.assertTrue(hasattr(a	,  "image_mean"		)		)
								self.assertTrue(hasattr(a	,  "image_std"		)		)







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					List[str]:
								__UpperCamelCase      :  List[str]       =   self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size	,  {"height": 1_8, "width": 2_0}		)

								__UpperCamelCase      :  Optional[int]       =   self.image_processing_class.from_dict(self.image_processor_dict	,  size=4_2		)
								self.assertEqual(image_processor.size	,  {"height": 4_2, "width": 4_2}		)

								# Previous config had dimensions in (width, height) order
								__UpperCamelCase      :  Optional[Any]       =   self.image_processing_class.from_dict(self.image_processor_dict	,  size=(4_2, 8_4)		)
								self.assertEqual(image_processor.size	,  {"height": 8_4, "width": 4_2}		)







	def   _lowerCamelCase							(   self						:Dict		)						->					Tuple:
								pass







	@is_flaky()
	def   _lowerCamelCase							(   self						:int		)						->					Any:
								# Initialize image_processing
								__UpperCamelCase      :  Optional[Any]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								__UpperCamelCase      :  str       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a		)
								for image in image_inputs:
															self.assertIsInstance(a	,  Image.Image		)

								# Test not batched input
								__UpperCamelCase      :  Any       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )	,  )

								# Test batched
								__UpperCamelCase      :  List[str]       =   image_processing(a	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )	,  )







	@is_flaky()
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Any:
								# Initialize image_processing
								__UpperCamelCase      :  Optional[int]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								__UpperCamelCase      :  Optional[int]       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a	,  numpify=a		)
								for image in image_inputs:
															self.assertIsInstance(a	,  np.ndarray		)

								# Test not batched input
								__UpperCamelCase      :  List[Any]       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )	,  )

								# Test batched
								__UpperCamelCase      :  Any       =   image_processing(a	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )	,  )







	@is_flaky()
	def   _lowerCamelCase							(   self						:Dict		)						->					List[Any]:
								# Initialize image_processing
								__UpperCamelCase      :  Optional[int]       =   self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								__UpperCamelCase      :  str       =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=a	,  torchify=a		)
								for image in image_inputs:
															self.assertIsInstance(a	,  torch.Tensor		)

								# Test not batched input
								__UpperCamelCase      :  int       =   image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )	,  )

								# Test batched
								__UpperCamelCase      :  List[str]       =   image_processing(a	,  return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape	,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )	,  )

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_fnet import FNetTokenizer
else:
		lowercase    :   List[Any]						=			None

lowercase    :   Tuple						=			logging.get_logger(__name__)
lowercase    :   Optional[Any]						=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase    :   str						=			{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowercase    :   int						=			{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowercase    :   str						=			'▁'




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	VOCAB_FILES_NAMES
	_A								=	PRETRAINED_VOCAB_FILES_MAP
	_A								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=	['input_ids', 'token_type_ids']
	_A								=	FNetTokenizer







	def __init__(   self						:Optional[int]	,  a						:Union[str, Any]=None	,  a						:Optional[int]=None	,  a						:str=False	,  a						:Tuple=True	,  a						:Optional[Any]=True	,  a						:Optional[int]="<unk>"	,  a						:List[str]="[SEP]"	,  a						:Optional[Any]="<pad>"	,  a						:int="[CLS]"	,  a						:Dict="[MASK]"	,  **a						:Optional[int]	,  )						->					List[str]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								__UpperCamelCase      :  List[Any]       =   (
								    AddedToken(a	,  lstrip=a	,  rstrip=a	,  normalized=a		)
								    if isinstance(a	,  a		)
								    else mask_token
								)

								super().__init__(
								    a	,  tokenizer_file=a	,  do_lower_case=a	,  remove_space=a	,  keep_accents=a	,  unk_token=a	,  sep_token=a	,  pad_token=a	,  cls_token=a	,  mask_token=a	,  **a	,  )

								__UpperCamelCase      :  List[Any]       =   do_lower_case
								__UpperCamelCase      :  str       =   remove_space
								__UpperCamelCase      :  Dict       =   keep_accents
								__UpperCamelCase      :  Union[str, Any]       =   vocab_file
								__UpperCamelCase      :  List[str]       =   False if not self.vocab_file else True







	def   _lowerCamelCase							(   self						:Dict	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								__UpperCamelCase      :  List[str]       =   [self.sep_token_id]
								__UpperCamelCase      :  List[str]       =   [self.cls_token_id]
								if token_ids_a is None:
															return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								__UpperCamelCase      :  List[Any]       =   [self.sep_token_id]
								__UpperCamelCase      :  List[Any]       =   [self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
								return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def   _lowerCamelCase							(   self						:int	,  a						:str	,  a						:Optional[str] = None		)						->					Tuple[str]:
								if not os.path.isdir(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return
								__UpperCamelCase      :  str       =   os.path.join(
								    a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(a		):
															copyfile(self.vocab_file	,  a		)

								return (out_vocab_file,)